posted on 2024-03-14, 12:48authored byLeonardo Barneschi, Leonardo Rotondi, Daniele Padula
We present a deep learning model able to predict excited
singlet–triplet
gaps with a mean absolute error (MAE) of ≈20 meV to obtain
potential inverted singlet–triplet (IST) candidates. We exploit
cutting-edge spherical message passing graph neural networks designed
specifically for generating 3D graph representations in molecular
learning. In a nutshell, the model takes as input a list of unsaturated
heavy atom Cartesian coordinates and atomic numbers, producing singlet–triplet
gaps as output. We exploited available large data collections to train
the model on ≈40,000 heterogeneous density functional theory
(DFT) geometries with available ADC(2)/cc-pVDZ singlet–triplet
gaps. We ascertain the predictive power of the model from a quantitative
perspective obtaining predictions on a test set of ≈14,000
molecules, whose geometries have been generated at DFT level (the
same employed for the geometries in the training set), at GFN2-xTB
level, and through Molecular Mechanics. We notice performance degradation
upon switching to lower-quality geometries, with GFN2-xTB ones maintaining
satisfactory results (MAE ≈ 50 meV on GFN2-xTB geometries,
MAE ≈ 180 meV on generalized AMBER force field geometries),
hinting at caution when dealing with specific chemical classes. Finally,
we verify the performance of the model from the qualitative point
of view, obtaining predictions on a different data set of ≈15,000
molecules already used to identify new IST molecules. We obtained
predictions using both DFT and experimental X-ray geometries, with
results on IST candidates similar to those provided by quantum chemical
methods, with clear hints for the path toward improved performance.